code: def _large_linear_regression():
      X = onnx.helper.make_tensor_value_info(
          "X", onnx.TensorProto.FLOAT, [None, None]
      )
      Y = onnx.helper.make_tensor_value_info(
          "Y", onnx.TensorProto.FLOAT, [None]
      )
      graph = onnx.helper.make_graph(
          [
              onnx.helper.make_node("MatMul", ["X", "A"], ["XA"]),
              onnx.helper.make_node("MatMul", ["XA", "B"], ["XB"]),
              onnx.helper.make_node("MatMul", ["XB", "C"], ["Y"]),
          ],
          "mm",
          [X],
          [Y],
          [
              onnx.model_container.make_large_tensor_proto(
                  "#loc0", "A", onnx.TensorProto.FLOAT, (3, 3)
              ),
              onnx.numpy_helper.from_array(
                  np.arange(9).astype(np.float32).reshape((-1, 3)),
                  name="B",
              ),
              onnx.model_container.make_large_tensor_proto(
                  "#loc1", "C", onnx.TensorProto.FLOAT, (3, 3)
              ),
          ],
      )
      onnx_model = onnx.helper.make_model(graph)
      large_model = onnx.model_container.make_large_model(
          onnx_model.graph,
          {
              "#loc0": (np.arange(9) * 100)
              .astype(np.float32)
              .reshape((-1, 3)),
              "#loc1": (np.arange(9) + 10)
              .astype(np.float32)
              .reshape((-1, 3)),
          },
      )
      large_model.check_model()
      return large_model
  
file_path: onnx/onnx/test/model_container_refeval_test.py
imports: import os
  import tempfile
  import unittest
  import numpy
  import numpy.testing
  import onnx
  import onnx.helper
  import onnx.model_container
  import onnx.numpy_helper
  import onnx.reference
label_desc: Use image node_id 2 for calling a global function with example usage: _large_linear_regression() and returns: large_model